GRAM用随机性打破了递归模型的确定性瓶颈，做推理模型或搜索算法的研究者可以直接复现，做数独或组合优化应用的团队值得关注。
一种名为GRAM（生成式递归推理）的新模型，仅用1000万参数，通过同时探索多条推理路径，在硬数独谜题上达到97%准确率，超越此前最佳递归模型（87.4%）。传统递归模型是确定性的，容易陷入错误轨迹，而GRAM在每个推理步骤注入随机性，生成多样化的推理路径，并在测试时并行运行并选择最佳结果。在N皇后等多解任务中，GRAM保持近乎完美的准确率，而确定性模型随解数量增加而崩溃。此外，GRAM还能作为生成器，用16步生成有效数独谜题，成功率99%，远超扩散模型。
A 10 million parameter model just outperformed det…
A 10 million parameter model just outperformed deterministic rivals 3 times its size by doing something regular recursive AI dont do: exploring multiple reasoning paths at the same time.
Most AI reasoning models are trapped on a single train of thought, and GRAM ("Generative Recursive Reasoning") is the first to break that by letting the model think in parallel universes simultaneously.
The problem is that all existing recursive models are fully deterministic, meaning given the same input they always follow the exact same reasoning path and can never escape a wrong trajectory or discover more than 1 valid answer.
GRAM fixes this by injecting learned randomness at each refinement step, so the model samples a slightly different direction each time rather than snapping to 1 fixed next state, which produces a spread of diverse reasoning trajectories.
At test time the model runs many of these paths in parallel and selects the best one using a small reward predictor trained alongside the main model, adding a "width" scaling axis on top of the usual "depth" axis of running more recursion steps.
On hard Sudoku puzzles, GRAM with 10M parameters hits 97% accuracy versus 87.4% for the best prior recursive model, and with only 20 parallel samples it outperforms every deterministic baseline even at 320 recursion steps.
On tasks with many valid answers like N-Queens, deterministic recursive models collapse as the number of solutions grows, while GRAM maintains near-perfect accuracy throughout.
The same stochastic framework also acts as a generator: given a blank board, GRAM produces valid Sudoku puzzles 99% of the time using 16 steps, versus 1,000 steps and 55M parameters for the best diffusion baseline at just 91%.
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Paper Link – arxiv. org/abs/2605.19376v1